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Research Hub/AI Operations Architecture

AI Operations Architecture

The 5-layer stack for production AI

TL;DR

AI Ops has crystallized into a 5-layer stack: Infrastructure, Model Gateway, Agent Orchestration, Memory Systems, and Observability. Organizations progress through 6 maturity levels from ad-hoc to autonomous. The key differentiator is memory architecture — working, episodic, semantic, and procedural.

Updated 2026-01-2711 sources validated4 claims verified

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Architecture layers

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6

Maturity levels

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Memory types

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Research files

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01

The 5-Layer AI Ops Stack

Production AI systems require a structured stack. Each layer addresses a specific concern, and skipping layers leads to failure at scale.

Layer 1: Infrastructure

Foundation

GPU clusters, model hosting, API management, cost controls

Layer 2: Model Gateway

Routing

Unified API routing (LiteLLM/Portkey), failover, rate limiting, cost tracking

Layer 3: Agent Orchestration

Logic

Multi-agent frameworks, workflow engines, task decomposition

Layer 4: Memory Systems

State

Working memory, episodic memory, semantic memory, procedural memory

Layer 5: Observability

Visibility

Tracing, evaluation, monitoring, alerting, drift detection

02

Memory Architecture

The most underappreciated aspect of production AI is memory. Four types of memory serve different purposes: working memory (current context), episodic memory (past interactions), semantic memory (knowledge base), and procedural memory (learned procedures). Effective systems combine all four.

Working Memory

Short-term

Active context window. What the agent is currently thinking about.

Episodic Memory

Experience

Past interactions and outcomes. Enables learning from experience.

Semantic Memory

Knowledge

Structured knowledge base. Facts, relationships, domain knowledge.

Procedural Memory

Skills

Learned workflows and procedures. How to accomplish specific tasks.

03

Maturity Model

Organizations progress through 6 levels: Level 0 (Ad-hoc, no structure), Level 1 (Basic, single agents), Level 2 (Managed, multi-agent with monitoring), Level 3 (Optimized, automated evaluation), Level 4 (Proactive, predictive systems), Level 5 (Autonomous, self-healing agent teams).

Key Findings

1

The 5-layer AI Ops stack provides a structured approach to production AI infrastructure

2

Memory architecture (4 types) is the key differentiator in production agent systems

3

Most organizations are at Level 1-2 of the 6-level maturity model

4

Model gateway architecture reduces vendor lock-in and enables automatic failover

5

Observability is the most commonly skipped layer — and the primary cause of production failures

Frequently Asked Questions

Infrastructure, Model Gateway, Agent Orchestration, Memory Systems, and Observability. Each layer addresses a specific concern for production AI.

Sources & References